本文選題：智能交通　切入點(diǎn)：交通流預(yù)測(cè)　出處：《沈陽大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著我國(guó)城市化進(jìn)程的加快，城市機(jī)動(dòng)車保有量持續(xù)增長(zhǎng)，交通問題日益突出。智能交通系統(tǒng)已成為解決城市交通問題的主要途徑。交通流預(yù)測(cè)是智能交通系統(tǒng)中的一項(xiàng)關(guān)鍵技術(shù)，主要用來實(shí)現(xiàn)城市交通路網(wǎng)中的各個(gè)節(jié)點(diǎn)和線路交通流量狀態(tài)的預(yù)測(cè)與分析。交通流預(yù)測(cè)的準(zhǔn)確性是實(shí)現(xiàn)城市交通流控制的前提條件。 交通流是一個(gè)多變量、時(shí)變的、結(jié)構(gòu)復(fù)雜的非線性系統(tǒng)。傳統(tǒng)的單預(yù)測(cè)模型只能概括系統(tǒng)的部分特性，預(yù)測(cè)精度受限。鑒于此，本文提出了基于相關(guān)路口分析和灰色神經(jīng)網(wǎng)絡(luò)的城市道路交通流量的組合預(yù)測(cè)方法，具體針對(duì)交通流預(yù)測(cè)模型、方法和實(shí)現(xiàn)進(jìn)行了較系統(tǒng)的研究，主要工作包括以下幾個(gè)方面： （1）提出了較系統(tǒng)的交通數(shù)據(jù)預(yù)處理方法，給出了數(shù)據(jù)錯(cuò)誤、丟失、冗余的判斷方法和處理過程。利用該方法可以有效去除噪聲數(shù)據(jù)的干擾，減少數(shù)據(jù)冗余，從而提高后續(xù)交通流預(yù)測(cè)的效率和準(zhǔn)確性。 （2）結(jié)合灰色系統(tǒng)和神經(jīng)網(wǎng)絡(luò)理論的各自優(yōu)勢(shì)，建立了灰色神經(jīng)網(wǎng)絡(luò)模型。利用該模型對(duì)城市道路交通流量進(jìn)行預(yù)測(cè)，仿真結(jié)果表明該模型可有效提高預(yù)測(cè)的精度和實(shí)時(shí)性。 （3）提出了一種路網(wǎng)路口流量相關(guān)性分析的方法。基于歷史數(shù)據(jù)，通過主成分分析法對(duì)路網(wǎng)目標(biāo)路口進(jìn)行流量相關(guān)性分析。建立了一種組合預(yù)測(cè)模型，利用路網(wǎng)相關(guān)路口數(shù)據(jù)預(yù)測(cè)數(shù)據(jù)缺失路口交通流量。 以城市道路路口為單位，，根據(jù)實(shí)際路況，同時(shí)考慮路口間流量大小及路口間隔長(zhǎng)度，以沈陽市部分行政區(qū)域路口為例建立局域路網(wǎng)。利用相關(guān)路口和目標(biāo)路口歷史數(shù)據(jù)，對(duì)預(yù)測(cè)模型、方法和實(shí)現(xiàn)進(jìn)行仿真實(shí)驗(yàn)，驗(yàn)證了其準(zhǔn)確性和實(shí)時(shí)性。
[Abstract]:With the acceleration of urbanization in China, the number of motor vehicles in urban areas continues to grow. Traffic problems are becoming more and more prominent. Intelligent Transportation system (its) has become the main way to solve urban traffic problems. Traffic flow forecasting is a key technology in Intelligent Transportation system (its). It is mainly used to predict and analyze the traffic flow state of each node and route in the urban traffic network, and the accuracy of the traffic flow prediction is the precondition to realize the urban traffic flow control. Traffic flow is a multivariable, time-varying and complex nonlinear system. The traditional single prediction model can only generalize some of the characteristics of the system, and the prediction accuracy is limited. In this paper, the combined forecasting method of urban road traffic flow based on correlation intersection analysis and grey neural network is put forward. The model, method and realization of traffic flow forecasting are studied systematically. The main work includes the following aspects:. In this paper, a systematic method of traffic data preprocessing is put forward, and the judgment method and processing process of data error, loss and redundancy are given. By using this method, the interference of noise data can be effectively removed and the data redundancy can be reduced. In order to improve the efficiency and accuracy of subsequent traffic flow prediction. Combining the respective advantages of grey system and neural network theory, the grey neural network model is established. The model is used to forecast the urban road traffic flow. The simulation results show that the model can effectively improve the accuracy and real-time performance of the prediction. Based on historical data and principal component analysis (PCA), a combined forecasting model is established. Using road network related intersection data to predict the traffic flow of missing intersection. Taking urban road junctions as units, according to the actual road conditions, considering the volume of intersections and the length of intersections, taking some administrative district junctions in Shenyang as an example, the local road network is established. The historical data of relevant intersections and target junctions are used. Simulation experiments on prediction model, method and implementation are carried out to verify its accuracy and real-time performance.
【學(xué)位授予單位】：沈陽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.14;TP183

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